Do You Know Which Key Drivers Change from Wave to Wave in Your Tracking Surveys? (302796)

Tracking surveys are common in applied research. The survey research situation is complicated when clients have a long list of measures they want used to assess key drivers of customer satisfaction, net promoter scores, or market share. Clients many times want to know if changes in individual key drivers are significantly different from wave to wave. In an example of 27 measures defining five key drivers, principal components analysis is used to optimally combine multiple measures of each key driver for use in multiple regression involving a biopharmaceutical in a duopoly market. A test for statistical significance is done following Snedecor and Cochran (1967, pages 432-436) to see if an observed difference in beta weights from one wave to the next is significant. The calculations for significance testing can be done in Excel using a straightforward formula calculating a t-test with [(n1 + n2) – 4] degrees of freedom calculated under the assumption of homogeneity of variance from one wave to the next. The ratio of the difference in beta weights to the pooled, or average, standard error calculates the t-test for significance testing.